The considerable overlap between welfare and child welfare service populations is well documented. Children from welfare families account for as much as 45 percent of those served by the child welfare system (American Humane Association, 1984). The strong association between welfare and child maltreatment may be due to a number of factors, including the stresses associated with poverty, the existence of concurrent risk factors such as mental illness and illicit drugs, and welfare recipients' more frequent contact with public authorities (Coulton et al., 1995; Gelles, 1992; Gil, 1971; Giovannoni and Billingsley, 1970; Wolock and Magura, 1996; Zuravin and DiBlasio, 1996).
Given the documented association between welfare and child maltreatment, a number of authors have reflected on the possible impacts of welfare reform on child welfare (Aber et al., 1995; Haskins, 1995; Meezan and Giovannoni, 1995; Wilson et al., 1995; Zaslow et al., 1995). Essentially all conclude that efforts to induce welfare mothers to self-sufficiency may impact rates of child maltreatment. Again, whether this impact is positive or negative depends in part on what effect reforms have on family income, parental stress, and access to services (Collins and Aber, 1996). For example, loss of benefits or other income supports such as Supplemental Security Income may strain a family's abilities to provide basic necessities such as food and shelter, causing increased neglect and homelessness, even abandonment (Collins, 1997; Knitzer and Bernard, 1997; Shook, 1998). Increased parental stress related to economic, employment, or childcare difficulties may also lead to increased rates of abuse (Knitzer and Bernard, 1997; Meezan and Giovannoni, 1995).
In contrast, positive changes in these areas may be favorable to children and families. For example, rates of abuse and neglect may decline if reforms reduce family's economic hardship. Additionally, gainful employment might improve the mental health of single mothers thereby decreasing the risk of child maltreatment (Garfinkel and McLanahan, 1986). Better access to mental health and drug services also might have similar effects. In addition to impacting the actual rates of maltreatment, the increased scrutiny by public authorities faced by TANF participants and their families might result in greater detection of previously unreported abuse and neglect. Whether positive or negative, these changes likely will be reflected in the number and types of maltreatment reports, the number of case investigations and substantiations, and the number of children placed in foster care.
Welfare reform also may affect the experiences of the children served by the child welfare system. With the passage of PRWORA, a family's economic circumstances become a critical component of the child welfare decision-making process. In particular, parental TANF status could influence the decision to remove a child from a sanctioned parent without any legitimate source of income, and if removed, the TANF status of potential kin caregivers might alter the subsequent placement decision (Zeller, 1998). For example, the proportion of kin placements might decline because kin caregivers might not be exempt from TANF requirements (Berrick et al., 1999; Geen and Waters, 1997; Boots and Green, 1999). Economic factors also might influence children's length of stay in foster care, placement stability, as well as their rates and types of exits from the system. Specifically, parental TANF status might facilitate or stall reunification efforts impacting the duration of children's out-of-home placements. Children placed with kin might experience placement disruptions if their TANF status changes. Although the impact of TANF noncompliance on reunification efforts is clear, compliance also might be problematic, with work making it difficult for parents to meet child welfare timelines such as visitation and court appearances (Knitzer and Bernard, 1997).
In addition to these potential impacts on exit rates, changes in a family's TANF status following reunification might lead to an increased likelihood of reabuse and child welfare system recidivism. Recent research on the child welfare experiences of families in Cleveland suggests that families that go on and off of welfare are more likely to fail in their attempts with reunification of their children than families that continuously receive welfare during the reunification period (Wells and Guo, in press). This, along with data from California (Needell et al., 1999) showing that AFDC families with breaks in AFDC receipt are more likely to become involved with child welfare services, suggests the substantial sensitivity of welfare families to changes in service circumstances.
Unlike the domain of child health, child welfare data traditionally have offered little uniform program participation data. Relevant data are collected only by state child protective service and foster care service departments. In some states (e.g., California) all child welfare administrative data are now entered into one data system. In most states, however, child abuse and neglect reporting and investigation data are gathered separately from data about foster care and adoption. The following section provides an overview of different configurations of these data sources that can be utilized to assess the impact of welfare reform on child maltreatment rates and children's experiences in and exits from the child welfare system. Access and confidentiality issues loom large when using such data to study vulnerable children. Readers should consult Brady and his colleagues (1999, this volume) for an in-depth review of these important topics.
Child Welfare Services Indicators
Most administrative data in the child welfare domain is composed of service event types and dates that can be configured to construct a variety of outcome indicators. The two most common configurations are descriptions of caseloads at a point in time (or several points in time) and longitudinal data analyses of individual service careers over time. In addition to program participation data, demographic data for the children and families under study (e.g., birthdate, ethnicity, home address or location) are also common elements found in these databases. When combined with these demographic data, caseload and longitudinal indicators can provide a source for estimating system performance and client status.
Caseload data provide a snapshot of welfare and child welfare at a specific point in time. They are usually used for program management purposes and can contribute to the assessment of system impacts by indicating covariation between subpopulations in welfare and child welfare. Broadly, for instance, caseload indicators of how many children of a certain age are leaving welfare and how many children of a certain age are entering foster care can provide some indication of whether the welfare exits might be contributing to increases in foster care. However, given the large size of the welfare caseload and the small numbers of children entering foster care, this relationship could not be adequately understood without individual-level data that linked welfare and child welfare histories.
Recently the ACF's Child Welfare Outcomes and Measures Project developed a set of outcome measures using point-in-time data from the Adoption and Foster Care Analysis and Reporting System (AFCARS) to assess state performance in operating child welfare programs. Outcomes include annual incidence of child maltreatment, types of exits from the child welfare system, timing of exits, and placement stability. Although point-in-time data also can be used to measure case status outcomes such as foster care length of stay; the resulting statistics are biased because they overrepresent children with longer stays in care and are not very sensitive to changes in entries to foster care because the newcomers to care are just a portion of the overall population. Thus, although point-in-time estimates are the easiest and least expensive configuration of administrative data, this inherent bias limits their usefulness until the individual records comprising these caseload data are reconfigured into longitudinal data.
Administrative data typically can be reconfigured into event-level files that record program participation histories. Depending on the scope of available data, these events may be restricted to foster care spells or placements, or may more broadly include child abuse reports, investigations, and services provided in the home. Working with entry cohorts provides the clearest evidence of changes in patterns of care that might be associated with changes in welfare programs because the interpretation of the outcomes does not have to disentangle the contributions of different service programs (e.g., AFDC and TANF). Using data that can be subset into entry cohorts captures the dynamics of both system entries and exits, and therefore provides a more accurate assessment of outcomes than caseload. Although free from the biases of point-in-time data, longitudinal data analyses often are preceded by considerable programming to reconfigure data into an even-level, longitudinal format, and to link welfare and child welfare files.
The Multistate Foster Care Data Archive provides an illustration of the complexity as well as promise that longitudinal data offer researchers trying to understand child welfare careers (and how they might be influenced by TANF). The archive is an initiative by the Children's Bureau of the U.S. Department of Health and Human Services that is designed to foster increased collaboration among states regarding administrative data collection in the child welfare services arena. Administered by the Chapin Hall Center for Children at the University of Chicago, the archive currently includes data from child welfare agencies in 11 states. The archive processes state data to make them comparable across state systems. To ensure data comparability, the project focuses on "a limited set of characteristics and events that have clear meaning in all jurisdictions" (Wulczyn et al., 1999:1). The core of the archive is two databases--one consisting of child records, including unique identifiers and demographic information, and a second event-level field that stores information on child welfare events of interest. This structure allows researchers to use the data in a longitudinal format to capture children's spells in child welfare as well as other experiences. Additionally, data can be configured to provide traditional point-in-time estimates of caseload flow over time.
A sufficiently comprehensive set of outcome indicators is shown in Box 10-1 (note that some indicators have a clearer theoretical relationship to welfare reform than others).
Minimum Child Welfare Services Indicators
- Child maltreatment reports (with reason for report)
- Case investigation (with reason for not investigating)
- Case substantiations (with reasons for providing services or not)
- In-home services (duration and frequency of provisions)
- Foster care placements (with placement dates and type of placement)
- Placement moves
- Foster care exits (with type of exit)
- Reentry to foster (with reason for reentry)
Depending on the purpose of the analysis, indicators can be derived from either point-in-time or longitudinal data. Indicators can be expressed as rates based on the number of people at risk in a state, county, and even zip code of the underlying populations, such as the foster care incidence (entry) rates and prevalence (caseload) rates by age and ethnicity. Benchmarks can be set for both caseload and longitudinal indicators, such as prevalence rate over time, or number and proportion of children who experience reabuse within a year of being reunified from foster care.
In anticipation of later analyses of the effects of welfare reform, researchers in several states have undertaken projects using linked longitudinal AFDC and child welfare data to better understand the overlap between these two programs. These projects serve as models of what will be possible with post-TANF data. In one such endeavor, the Child Welfare Research Center at the University of California at Berkeley undertook an analysis to identify the characteristics of poor families at risk of child maltreatment. Using data from the California Children's Services Archive, researchers constructed a longitudinal database of children entering AFDC between 1988 and 1995 using MediCal data in 10 counties. Probability-matching software was employed to link AFDC histories for these children with birth records, statewide foster care data, and child maltreatment reporting data. Results revealed substantial overlap between the welfare and child welfare populations, with approximately 27 percent of all 1990 child AFDC entrants having child welfare contact, within 5 years and 3 percent entering foster care. This indicates that the overlap between welfare and child protective services is large enough to allow modeling of changes over time and across program types, although analyses of transitions to foster care may be too few to allow powerful modeling. Both total time on aid as well as the number of spells on aid were associated with child welfare contact. Children who transitioned to the child welfare system were more likely to come from single-parent families, larger families, have low birthweight and late or no prenatal care (Needell et al., 1999).
A similar analysis was undertaken in Illinois at the University of Chicago's Chapin Hall Center for Children. Using linked longitudinal data from the state Department of Children and Family Services and the Division of Financial Support Services, Shook (1998) set out to identify baseline rates of maltreatment among children in the Illinois AFDC program between 1990 and 1995. She also identified risk factors for child welfare contact among this population. Risks were higher for children on nonparent cases, children from single-parent families, and white children. Of particular interest were the findings that transitions were more likely among children with sanctioned family grants, because child removals for neglect, lack of supervision, or risk of harm were more likely among sanctioned cases. In addition to helping to identify possible implications of TANF sanctions, the research highlights the use of linked administrative data in assessing the impact of welfare reform on child welfare.
Administrative data from child welfare records also have been combined with qualitative survey data to study the impact of welfare reform. For example, a study currently under way (a collaborative effort by The Urban Institute and The University of California at Berkeley's Center for Social Services Research and UC Data Archive and Technical Assistance, funded by the Stuart Foundation) will combine qualitative data of welfare recipients with data from their administrative welfare records and any available data on children in the home that exist in child welfare administrative data records. This "marriage" between administrative data and qualitative data holds great promise. Specifically, although administrative data can provide information to answer questions such as "How many? What proportion? How long?", other methods can shed some light as to "Why?"
The Center for Social Services Research at the University of California at Berkeley routinely has used child welfare administrative data to draw representative samples to study children using other research methods. For example, counties in the Bay Area Social Services Consortium have funded research to understand the educational needs of children in foster care. A random sample of caregivers drawn from administrative data records is being interviewed to gather detailed information about the children in their care. Similarly, case records of children have been reviewed to look at concurrent planning in child welfare. (Concurrent planning is the provision of an alternative permanent plan, such as adoption, simultaneously with efforts to return a child to his or her birthparent.) In both cases, the samples were drawn from administrative data. Such methods easily could be adapted to provide more in-depth analysis of critical welfare reform issues.
Despite the wide variety of outcome indicators that can be configured from child welfare administrative data, like all services data, child welfare data cover only those who receive services. Because child welfare data are available only for those abused and neglected children who come to the attention of public systems of care, changes to the undetected abuse rate that may result from welfare program changes cannot be assessed. Despite the hurdles associated with linking welfare and child welfare data, given the established association between poverty and maltreatment, child welfare advocates and policy makers must examine the impact of welfare reform on child welfare services. In particular, whether these changes increase the likelihood of maltreatment has important consequences for both the TANF families and children as well as the general social good. In addition to the immediate risk of physical harm and even death maltreated children face, longer term consequences include deficits in emotional and physical health, cognitive development, and socialization difficulties (Ammerman et al., 1986; Couch and Milner, 1993). Furthermore, observed relationships between childhood maltreatment and later criminal activity or abusive behavior also increase future consequences for both children and society (Gray, 1988; Jonson-Reid and Barth, 2000).